{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**目标：根据历史数据，预测当天股票最高价**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模块导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-11T09:51:30.853525Z",
     "start_time": "2019-03-11T09:51:30.247125Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "from torch.utils.data import Dataset, DataLoader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据读取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 原始数据获取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-11T09:51:34.178777Z",
     "start_time": "2019-03-11T09:51:31.219983Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>p_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01-04</th>\n",
       "      <td>000300</td>\n",
       "      <td>994.76</td>\n",
       "      <td>982.79</td>\n",
       "      <td>994.76</td>\n",
       "      <td>980.65</td>\n",
       "      <td>74128.0</td>\n",
       "      <td>4.431976e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-05</th>\n",
       "      <td>000300</td>\n",
       "      <td>981.57</td>\n",
       "      <td>992.56</td>\n",
       "      <td>997.32</td>\n",
       "      <td>979.87</td>\n",
       "      <td>71191.0</td>\n",
       "      <td>4.529207e+09</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-06</th>\n",
       "      <td>000300</td>\n",
       "      <td>993.33</td>\n",
       "      <td>983.17</td>\n",
       "      <td>993.78</td>\n",
       "      <td>980.33</td>\n",
       "      <td>62880.0</td>\n",
       "      <td>3.921015e+09</td>\n",
       "      <td>-0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-07</th>\n",
       "      <td>000300</td>\n",
       "      <td>983.04</td>\n",
       "      <td>983.95</td>\n",
       "      <td>995.71</td>\n",
       "      <td>979.81</td>\n",
       "      <td>72986.0</td>\n",
       "      <td>4.737468e+09</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-10</th>\n",
       "      <td>000300</td>\n",
       "      <td>983.76</td>\n",
       "      <td>993.87</td>\n",
       "      <td>993.95</td>\n",
       "      <td>979.78</td>\n",
       "      <td>57916.0</td>\n",
       "      <td>3.762931e+09</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-11</th>\n",
       "      <td>000300</td>\n",
       "      <td>994.18</td>\n",
       "      <td>997.13</td>\n",
       "      <td>999.55</td>\n",
       "      <td>991.09</td>\n",
       "      <td>58490.0</td>\n",
       "      <td>3.704076e+09</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-12</th>\n",
       "      <td>000300</td>\n",
       "      <td>996.65</td>\n",
       "      <td>996.74</td>\n",
       "      <td>996.97</td>\n",
       "      <td>989.25</td>\n",
       "      <td>50145.0</td>\n",
       "      <td>3.093298e+09</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-13</th>\n",
       "      <td>000300</td>\n",
       "      <td>996.07</td>\n",
       "      <td>996.87</td>\n",
       "      <td>999.47</td>\n",
       "      <td>992.69</td>\n",
       "      <td>60440.0</td>\n",
       "      <td>3.842172e+09</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-14</th>\n",
       "      <td>000300</td>\n",
       "      <td>996.61</td>\n",
       "      <td>988.30</td>\n",
       "      <td>1006.46</td>\n",
       "      <td>987.23</td>\n",
       "      <td>72978.0</td>\n",
       "      <td>4.162920e+09</td>\n",
       "      <td>-0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-17</th>\n",
       "      <td>000300</td>\n",
       "      <td>979.11</td>\n",
       "      <td>967.45</td>\n",
       "      <td>981.52</td>\n",
       "      <td>965.07</td>\n",
       "      <td>72881.0</td>\n",
       "      <td>4.249807e+09</td>\n",
       "      <td>-2.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-18</th>\n",
       "      <td>000300</td>\n",
       "      <td>967.37</td>\n",
       "      <td>974.68</td>\n",
       "      <td>974.87</td>\n",
       "      <td>960.29</td>\n",
       "      <td>73118.0</td>\n",
       "      <td>4.117944e+09</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-19</th>\n",
       "      <td>000300</td>\n",
       "      <td>974.33</td>\n",
       "      <td>967.21</td>\n",
       "      <td>974.33</td>\n",
       "      <td>965.25</td>\n",
       "      <td>63380.0</td>\n",
       "      <td>3.427951e+09</td>\n",
       "      <td>-0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-20</th>\n",
       "      <td>000300</td>\n",
       "      <td>963.21</td>\n",
       "      <td>956.24</td>\n",
       "      <td>963.21</td>\n",
       "      <td>952.23</td>\n",
       "      <td>77271.0</td>\n",
       "      <td>4.399349e+09</td>\n",
       "      <td>-1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-21</th>\n",
       "      <td>000300</td>\n",
       "      <td>954.46</td>\n",
       "      <td>982.60</td>\n",
       "      <td>984.27</td>\n",
       "      <td>943.43</td>\n",
       "      <td>144500.0</td>\n",
       "      <td>8.152086e+09</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-24</th>\n",
       "      <td>000300</td>\n",
       "      <td>1001.85</td>\n",
       "      <td>998.13</td>\n",
       "      <td>1001.85</td>\n",
       "      <td>986.23</td>\n",
       "      <td>143594.0</td>\n",
       "      <td>8.360160e+09</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-25</th>\n",
       "      <td>000300</td>\n",
       "      <td>995.63</td>\n",
       "      <td>997.77</td>\n",
       "      <td>997.95</td>\n",
       "      <td>985.23</td>\n",
       "      <td>98625.0</td>\n",
       "      <td>6.157022e+09</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-26</th>\n",
       "      <td>000300</td>\n",
       "      <td>995.78</td>\n",
       "      <td>989.92</td>\n",
       "      <td>999.47</td>\n",
       "      <td>988.47</td>\n",
       "      <td>76632.0</td>\n",
       "      <td>4.719439e+09</td>\n",
       "      <td>-0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-27</th>\n",
       "      <td>000300</td>\n",
       "      <td>987.34</td>\n",
       "      <td>974.63</td>\n",
       "      <td>987.70</td>\n",
       "      <td>973.77</td>\n",
       "      <td>69453.0</td>\n",
       "      <td>4.094397e+09</td>\n",
       "      <td>-1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-28</th>\n",
       "      <td>000300</td>\n",
       "      <td>974.63</td>\n",
       "      <td>969.20</td>\n",
       "      <td>975.62</td>\n",
       "      <td>965.20</td>\n",
       "      <td>57488.0</td>\n",
       "      <td>3.280950e+09</td>\n",
       "      <td>-0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-31</th>\n",
       "      <td>000300</td>\n",
       "      <td>965.78</td>\n",
       "      <td>954.87</td>\n",
       "      <td>965.78</td>\n",
       "      <td>953.14</td>\n",
       "      <td>67887.0</td>\n",
       "      <td>3.863572e+09</td>\n",
       "      <td>-1.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              code     open   close     high     low       vol        amount  \\\n",
       "datetime                                                                       \n",
       "2005-01-04  000300   994.76  982.79   994.76  980.65   74128.0  4.431976e+09   \n",
       "2005-01-05  000300   981.57  992.56   997.32  979.87   71191.0  4.529207e+09   \n",
       "2005-01-06  000300   993.33  983.17   993.78  980.33   62880.0  3.921015e+09   \n",
       "2005-01-07  000300   983.04  983.95   995.71  979.81   72986.0  4.737468e+09   \n",
       "2005-01-10  000300   983.76  993.87   993.95  979.78   57916.0  3.762931e+09   \n",
       "2005-01-11  000300   994.18  997.13   999.55  991.09   58490.0  3.704076e+09   \n",
       "2005-01-12  000300   996.65  996.74   996.97  989.25   50145.0  3.093298e+09   \n",
       "2005-01-13  000300   996.07  996.87   999.47  992.69   60440.0  3.842172e+09   \n",
       "2005-01-14  000300   996.61  988.30  1006.46  987.23   72978.0  4.162920e+09   \n",
       "2005-01-17  000300   979.11  967.45   981.52  965.07   72881.0  4.249807e+09   \n",
       "2005-01-18  000300   967.37  974.68   974.87  960.29   73118.0  4.117944e+09   \n",
       "2005-01-19  000300   974.33  967.21   974.33  965.25   63380.0  3.427951e+09   \n",
       "2005-01-20  000300   963.21  956.24   963.21  952.23   77271.0  4.399349e+09   \n",
       "2005-01-21  000300   954.46  982.60   984.27  943.43  144500.0  8.152086e+09   \n",
       "2005-01-24  000300  1001.85  998.13  1001.85  986.23  143594.0  8.360160e+09   \n",
       "2005-01-25  000300   995.63  997.77   997.95  985.23   98625.0  6.157022e+09   \n",
       "2005-01-26  000300   995.78  989.92   999.47  988.47   76632.0  4.719439e+09   \n",
       "2005-01-27  000300   987.34  974.63   987.70  973.77   69453.0  4.094397e+09   \n",
       "2005-01-28  000300   974.63  969.20   975.62  965.20   57488.0  3.280950e+09   \n",
       "2005-01-31  000300   965.78  954.87   965.78  953.14   67887.0  3.863572e+09   \n",
       "\n",
       "            p_change  \n",
       "datetime              \n",
       "2005-01-04       NaN  \n",
       "2005-01-05      0.99  \n",
       "2005-01-06     -0.95  \n",
       "2005-01-07      0.08  \n",
       "2005-01-10      1.01  \n",
       "2005-01-11      0.33  \n",
       "2005-01-12     -0.04  \n",
       "2005-01-13      0.01  \n",
       "2005-01-14     -0.86  \n",
       "2005-01-17     -2.11  \n",
       "2005-01-18      0.75  \n",
       "2005-01-19     -0.77  \n",
       "2005-01-20     -1.13  \n",
       "2005-01-21      2.76  \n",
       "2005-01-24      1.58  \n",
       "2005-01-25     -0.04  \n",
       "2005-01-26     -0.79  \n",
       "2005-01-27     -1.54  \n",
       "2005-01-28     -0.56  \n",
       "2005-01-31     -1.48  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tushare as ts\n",
    "\n",
    "cons = ts.get_apis()\n",
    "df = ts.bar('000300', conn=cons, asset='INDEX', start_date='2002-01-01', end_date='')\n",
    "\n",
    "# 注意历史数据靠前\n",
    "df = df.sort_index(ascending=True)\n",
    "df.to_csv('sh.csv')\n",
    "\n",
    "# 可以看出，周末不进行交易\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-11T09:51:34.845435Z",
     "start_time": "2019-03-11T09:51:34.775938Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>p_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01-04</th>\n",
       "      <td>300</td>\n",
       "      <td>994.76</td>\n",
       "      <td>982.79</td>\n",
       "      <td>994.76</td>\n",
       "      <td>980.65</td>\n",
       "      <td>74128.0</td>\n",
       "      <td>4.431976e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-05</th>\n",
       "      <td>300</td>\n",
       "      <td>981.57</td>\n",
       "      <td>992.56</td>\n",
       "      <td>997.32</td>\n",
       "      <td>979.87</td>\n",
       "      <td>71191.0</td>\n",
       "      <td>4.529207e+09</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-06</th>\n",
       "      <td>300</td>\n",
       "      <td>993.33</td>\n",
       "      <td>983.17</td>\n",
       "      <td>993.78</td>\n",
       "      <td>980.33</td>\n",
       "      <td>62880.0</td>\n",
       "      <td>3.921015e+09</td>\n",
       "      <td>-0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-07</th>\n",
       "      <td>300</td>\n",
       "      <td>983.04</td>\n",
       "      <td>983.95</td>\n",
       "      <td>995.71</td>\n",
       "      <td>979.81</td>\n",
       "      <td>72986.0</td>\n",
       "      <td>4.737468e+09</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-10</th>\n",
       "      <td>300</td>\n",
       "      <td>983.76</td>\n",
       "      <td>993.87</td>\n",
       "      <td>993.95</td>\n",
       "      <td>979.78</td>\n",
       "      <td>57916.0</td>\n",
       "      <td>3.762931e+09</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            code    open   close    high     low      vol        amount  \\\n",
       "2005-01-04   300  994.76  982.79  994.76  980.65  74128.0  4.431976e+09   \n",
       "2005-01-05   300  981.57  992.56  997.32  979.87  71191.0  4.529207e+09   \n",
       "2005-01-06   300  993.33  983.17  993.78  980.33  62880.0  3.921015e+09   \n",
       "2005-01-07   300  983.04  983.95  995.71  979.81  72986.0  4.737468e+09   \n",
       "2005-01-10   300  983.76  993.87  993.95  979.78  57916.0  3.762931e+09   \n",
       "\n",
       "            p_change  \n",
       "2005-01-04       NaN  \n",
       "2005-01-05      0.99  \n",
       "2005-01-06     -0.95  \n",
       "2005-01-07      0.08  \n",
       "2005-01-10      1.01  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('sh.csv', index_col=0)\n",
    "df.index = list(map(lambda x:datetime.datetime.strptime(x, '%Y-%m-%d'), df.index))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-11T09:51:35.322705Z",
     "start_time": "2019-03-11T09:51:35.317791Z"
    }
   },
   "outputs": [],
   "source": [
    "def getData(df, column, train_end=-300, days_before=30, return_all=True, generate_index=False):\n",
    "    '''\n",
    "    读取原始数据，并生成训练样本\n",
    "    df             : 原始数据\n",
    "    column         : 要处理的列\n",
    "    train_end      : 训练集的终点\n",
    "    days_before    : 多少天来预测下一天\n",
    "    return_all     : 是否返回所有数据，默认 True\n",
    "    generate_index : 是否生成 index\n",
    "    '''\n",
    "    series = df[column].copy()\n",
    "    \n",
    "    # 划分数据\n",
    "    # 0 ~ train_end 的为训练数据，但实际上，最后的 n 天只是作为 label\n",
    "    # 而 train 中的 label，可用于 test\n",
    "    train_series, test_series = series[:train_end], series[train_end - days_before:]\n",
    "    \n",
    "    # 创建训练集\n",
    "    train_data = pd.DataFrame()\n",
    "        \n",
    "    # 通过移位，创建历史 days_before 天的数据\n",
    "    for i in range(days_before):\n",
    "        # 当前数据的 7 天前的数据，应该取 开始到 7 天前的数据； 昨天的数据，应该为开始到昨天的数据，如：\n",
    "        # [..., 1,2,3,4,5,6,7] 昨天的为 [..., 1,2,3,4,5,6]\n",
    "        # 比如从 [2:-7+2]，其长度为 len - 7\n",
    "        train_data['c%d' % i] = train_series.tolist()[i: -days_before + i]\n",
    "            \n",
    "    # 获取对应的 label\n",
    "    train_data['y'] = train_series.tolist()[days_before:]\n",
    "        \n",
    "    # 是否生成 index\n",
    "    if generate_index:\n",
    "        train_data.index = train_series.index[n:]\n",
    "                \n",
    "    if return_all:\n",
    "        return train_data, series, df.index.tolist()\n",
    "    \n",
    "    return train_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建 LSTM 层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-11T09:51:35.809188Z",
     "start_time": "2019-03-11T09:51:35.805087Z"
    }
   },
   "outputs": [],
   "source": [
    "class LSTM(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(LSTM, self).__init__()\n",
    "        \n",
    "        self.lstm = nn.LSTM(\n",
    "            input_size=1,   # 输入尺寸为 1，表示一天的数据\n",
    "            hidden_size=64,\n",
    "            num_layers=1, \n",
    "            batch_first=True)\n",
    "        \n",
    "        self.out = nn.Sequential(\n",
    "            nn.Linear(64,1))\n",
    "        \n",
    "    def forward(self, x):\n",
    "        r_out, (h_n, h_c) = self.lstm(x, None)   # None 表示 hidden state 会用全 0 的 state\n",
    "        out = self.out(r_out[:, -1, :])          # 取最后一天作为输出\n",
    "        \n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-11T09:51:36.294384Z",
     "start_time": "2019-03-11T09:51:36.291014Z"
    }
   },
   "outputs": [],
   "source": [
    "class TrainSet(Dataset):\n",
    "    def __init__(self, data):\n",
    "        # 定义好 image 的路径\n",
    "        # data 取前多少天的数据， label 取最后一天的数据\n",
    "        self.data, self.label = data[:, :-1].float(), data[:, -1].float()\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.data[index], self.label[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 超参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-11T09:51:36.828950Z",
     "start_time": "2019-03-11T09:51:36.826128Z"
    }
   },
   "outputs": [],
   "source": [
    "LR = 0.0001\n",
    "EPOCH = 100\n",
    "TRAIN_END=-300\n",
    "DAYS_BEFORE=7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获取数据\n",
    "- 训练模型仍然使用 minibatch 的思路\n",
    "- 注意，模型必须要先把数据标准化，不然损失会很难降低下来。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-11T09:51:38.622353Z",
     "start_time": "2019-03-11T09:51:38.272936Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/leon/TankZhou/env/Deeplearning/lib/python3.6/site-packages/pandas/plotting/_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 数据集建立\n",
    "train_data, all_series, df_index = getData(df, 'high', days_before=DAYS_BEFORE, train_end=TRAIN_END)\n",
    "\n",
    "# 获取所有原始数据\n",
    "all_series = np.array(all_series.tolist())\n",
    "# 绘制原始数据的图\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(df_index, all_series, label='real-data')\n",
    "\n",
    "# 归一化，便与训练\n",
    "train_data_numpy = np.array(train_data)\n",
    "train_mean = np.mean(train_data_numpy)\n",
    "train_std  = np.std(train_data_numpy)\n",
    "train_data_numpy = (train_data_numpy - train_mean) / train_std\n",
    "train_data_tensor = torch.Tensor(train_data_numpy)\n",
    "\n",
    "# 创建 dataloader\n",
    "train_set = TrainSet(train_data_tensor)\n",
    "train_loader = DataLoader(train_set, batch_size=10, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型训练部分\n",
    "\n",
    "这个部分如果是不想要训练的话（比如已经训练好了模型），替换为 rnn = torch.load('rnn.pkl') （记得把原来的注释掉）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-11T09:53:32.002744Z",
     "start_time": "2019-03-11T09:51:41.012806Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 tensor(0.1693, grad_fn=<CopyBackwards>)\n",
      "1 tensor(0.0172, grad_fn=<CopyBackwards>)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/leon/TankZhou/env/Deeplearning/lib/python3.6/site-packages/torch/serialization.py:241: UserWarning: Couldn't retrieve source code for container of type LSTM. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 tensor(0.0512, grad_fn=<CopyBackwards>)\n",
      "3 tensor(0.0151, grad_fn=<CopyBackwards>)\n",
      "4 tensor(0.0040, grad_fn=<CopyBackwards>)\n",
      "5 tensor(0.0087, grad_fn=<CopyBackwards>)\n",
      "6 tensor(0.0066, grad_fn=<CopyBackwards>)\n",
      "7 tensor(0.0140, grad_fn=<CopyBackwards>)\n",
      "8 tensor(0.0248, grad_fn=<CopyBackwards>)\n",
      "9 tensor(0.0212, grad_fn=<CopyBackwards>)\n",
      "10 tensor(0.0022, grad_fn=<CopyBackwards>)\n",
      "11 tensor(0.0038, grad_fn=<CopyBackwards>)\n",
      "12 tensor(0.0063, grad_fn=<CopyBackwards>)\n",
      "13 tensor(0.0012, grad_fn=<CopyBackwards>)\n",
      "14 tensor(0.0076, grad_fn=<CopyBackwards>)\n",
      "15 tensor(0.0105, grad_fn=<CopyBackwards>)\n",
      "16 tensor(0.0007, grad_fn=<CopyBackwards>)\n",
      "17 tensor(0.0024, grad_fn=<CopyBackwards>)\n",
      "18 tensor(0.0033, grad_fn=<CopyBackwards>)\n",
      "19 tensor(0.0063, grad_fn=<CopyBackwards>)\n",
      "20 tensor(0.0020, grad_fn=<CopyBackwards>)\n",
      "21 tensor(0.0030, grad_fn=<CopyBackwards>)\n",
      "22 tensor(0.0033, grad_fn=<CopyBackwards>)\n",
      "23 tensor(0.0127, grad_fn=<CopyBackwards>)\n",
      "24 tensor(0.0079, grad_fn=<CopyBackwards>)\n",
      "25 tensor(0.0051, grad_fn=<CopyBackwards>)\n",
      "26 tensor(0.0018, grad_fn=<CopyBackwards>)\n",
      "27 tensor(0.0028, grad_fn=<CopyBackwards>)\n",
      "28 tensor(0.0029, grad_fn=<CopyBackwards>)\n",
      "29 tensor(0.0028, grad_fn=<CopyBackwards>)\n",
      "30 tensor(0.0049, grad_fn=<CopyBackwards>)\n",
      "31 tensor(0.0040, grad_fn=<CopyBackwards>)\n",
      "32 tensor(0.0027, grad_fn=<CopyBackwards>)\n",
      "33 tensor(0.0137, grad_fn=<CopyBackwards>)\n",
      "34 tensor(0.0008, grad_fn=<CopyBackwards>)\n",
      "35 tensor(0.0064, grad_fn=<CopyBackwards>)\n",
      "36 tensor(0.0004, grad_fn=<CopyBackwards>)\n",
      "37 tensor(0.0007, grad_fn=<CopyBackwards>)\n",
      "38 tensor(0.0153, grad_fn=<CopyBackwards>)\n",
      "39 tensor(0.0086, grad_fn=<CopyBackwards>)\n",
      "40 tensor(0.0048, grad_fn=<CopyBackwards>)\n",
      "41 tensor(0.0076, grad_fn=<CopyBackwards>)\n",
      "42 tensor(0.0008, grad_fn=<CopyBackwards>)\n",
      "43 tensor(0.0043, grad_fn=<CopyBackwards>)\n",
      "44 tensor(0.0016, grad_fn=<CopyBackwards>)\n",
      "45 tensor(0.0011, grad_fn=<CopyBackwards>)\n",
      "46 tensor(0.0016, grad_fn=<CopyBackwards>)\n",
      "47 tensor(0.0008, grad_fn=<CopyBackwards>)\n",
      "48 tensor(0.0065, grad_fn=<CopyBackwards>)\n",
      "49 tensor(0.0007, grad_fn=<CopyBackwards>)\n",
      "50 tensor(0.0009, grad_fn=<CopyBackwards>)\n",
      "51 tensor(0.0019, grad_fn=<CopyBackwards>)\n",
      "52 tensor(0.0023, grad_fn=<CopyBackwards>)\n",
      "53 tensor(0.0029, grad_fn=<CopyBackwards>)\n",
      "54 tensor(0.0017, grad_fn=<CopyBackwards>)\n",
      "55 tensor(0.0051, grad_fn=<CopyBackwards>)\n",
      "56 tensor(0.0049, grad_fn=<CopyBackwards>)\n",
      "57 tensor(0.0034, grad_fn=<CopyBackwards>)\n",
      "58 tensor(0.0016, grad_fn=<CopyBackwards>)\n",
      "59 tensor(0.0031, grad_fn=<CopyBackwards>)\n",
      "60 tensor(0.0043, grad_fn=<CopyBackwards>)\n",
      "61 tensor(0.0034, grad_fn=<CopyBackwards>)\n",
      "62 tensor(0.0008, grad_fn=<CopyBackwards>)\n",
      "63 tensor(0.0020, grad_fn=<CopyBackwards>)\n",
      "64 tensor(0.0013, grad_fn=<CopyBackwards>)\n",
      "65 tensor(0.0019, grad_fn=<CopyBackwards>)\n",
      "66 tensor(0.0031, grad_fn=<CopyBackwards>)\n",
      "67 tensor(0.0004, grad_fn=<CopyBackwards>)\n",
      "68 tensor(0.0017, grad_fn=<CopyBackwards>)\n",
      "69 tensor(0.0015, grad_fn=<CopyBackwards>)\n",
      "70 tensor(0.0009, grad_fn=<CopyBackwards>)\n",
      "71 tensor(0.0009, grad_fn=<CopyBackwards>)\n",
      "72 tensor(0.0017, grad_fn=<CopyBackwards>)\n",
      "73 tensor(0.0002, grad_fn=<CopyBackwards>)\n",
      "74 tensor(0.0007, grad_fn=<CopyBackwards>)\n",
      "75 tensor(0.0026, grad_fn=<CopyBackwards>)\n",
      "76 tensor(0.0030, grad_fn=<CopyBackwards>)\n",
      "77 tensor(0.0027, grad_fn=<CopyBackwards>)\n",
      "78 tensor(0.0037, grad_fn=<CopyBackwards>)\n",
      "79 tensor(0.0021, grad_fn=<CopyBackwards>)\n",
      "80 tensor(0.0012, grad_fn=<CopyBackwards>)\n",
      "81 tensor(0.0012, grad_fn=<CopyBackwards>)\n",
      "82 tensor(0.0027, grad_fn=<CopyBackwards>)\n",
      "83 tensor(0.0027, grad_fn=<CopyBackwards>)\n",
      "84 tensor(0.0017, grad_fn=<CopyBackwards>)\n",
      "85 tensor(0.0007, grad_fn=<CopyBackwards>)\n",
      "86 tensor(0.0047, grad_fn=<CopyBackwards>)\n",
      "87 tensor(0.0016, grad_fn=<CopyBackwards>)\n",
      "88 tensor(0.0037, grad_fn=<CopyBackwards>)\n",
      "89 tensor(0.0013, grad_fn=<CopyBackwards>)\n",
      "90 tensor(0.0007, grad_fn=<CopyBackwards>)\n",
      "91 tensor(0.0048, grad_fn=<CopyBackwards>)\n",
      "92 tensor(0.0081, grad_fn=<CopyBackwards>)\n",
      "93 tensor(0.0048, grad_fn=<CopyBackwards>)\n",
      "94 tensor(0.0010, grad_fn=<CopyBackwards>)\n",
      "95 tensor(0.0035, grad_fn=<CopyBackwards>)\n",
      "96 tensor(0.0006, grad_fn=<CopyBackwards>)\n",
      "97 tensor(0.0028, grad_fn=<CopyBackwards>)\n",
      "98 tensor(0.0025, grad_fn=<CopyBackwards>)\n",
      "99 tensor(0.0018, grad_fn=<CopyBackwards>)\n"
     ]
    }
   ],
   "source": [
    "rnn = LSTM()\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    rnn = rnn.cuda()\n",
    "\n",
    "optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)  # optimize all cnn parameters\n",
    "loss_func = nn.MSELoss()\n",
    "\n",
    "for step in range(EPOCH):\n",
    "    for tx, ty in train_loader:\n",
    "        \n",
    "        if torch.cuda.is_available():\n",
    "            tx = tx.cuda()\n",
    "            ty = ty.cuda()       \n",
    "        \n",
    "        output = rnn(torch.unsqueeze(tx, dim=2))\n",
    "        loss = loss_func(torch.squeeze(output), ty)\n",
    "        optimizer.zero_grad()  # clear gradients for this training step\n",
    "        loss.backward()  # back propagation, compute gradients\n",
    "        optimizer.step()\n",
    "    print(step, loss.cpu())\n",
    "    if step % 10:\n",
    "        torch.save(rnn, 'rnn.pkl')\n",
    "torch.save(rnn, 'rnn.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 画图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-11T09:53:54.982541Z",
     "start_time": "2019-03-11T09:53:52.788444Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_data_train = []\n",
    "generate_data_test = []\n",
    "\n",
    "# 测试数据开始的索引\n",
    "test_start = len(all_series) + TRAIN_END\n",
    "\n",
    "# 对所有的数据进行相同的归一化\n",
    "all_series = (all_series - train_mean) / train_std\n",
    "all_series = torch.Tensor(all_series)\n",
    "\n",
    "for i in range(DAYS_BEFORE, len(all_series)):\n",
    "    x = all_series[i - DAYS_BEFORE:i]\n",
    "    # 将 x 填充到 (bs, ts, is) 中的 timesteps\n",
    "    x = torch.unsqueeze(torch.unsqueeze(x, dim=0), dim=2)\n",
    "    \n",
    "    if torch.cuda.is_available():\n",
    "        x = x.cuda()\n",
    "\n",
    "    y = rnn(x)\n",
    "    \n",
    "    if i < test_start:\n",
    "        generate_data_train.append(torch.squeeze(y.cpu()).detach().numpy() * train_std + train_mean)\n",
    "    else:\n",
    "        generate_data_test.append(torch.squeeze(y.cpu()).detach().numpy() * train_std + train_mean)\n",
    "        \n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(df_index[DAYS_BEFORE: TRAIN_END], generate_data_train, 'b', label='generate_train', )\n",
    "plt.plot(df_index[TRAIN_END:], generate_data_test, 'k', label='generate_test')\n",
    "plt.plot(df_index, all_series.clone().numpy()* train_std + train_mean, 'r', label='real_data')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-11T10:02:50.818515Z",
     "start_time": "2019-03-11T10:02:50.383086Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x1152 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,16))\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(df_index[100 + DAYS_BEFORE: 130 + DAYS_BEFORE], generate_data_train[100: 130], 'b', label='generate_train')\n",
    "plt.plot(df_index[100 + DAYS_BEFORE: 130 + DAYS_BEFORE], (all_series.clone().numpy()* train_std + train_mean)[100 + DAYS_BEFORE: 130 + DAYS_BEFORE], 'r', label='real_data')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(df_index[TRAIN_END + 50: TRAIN_END + 80], generate_data_test[50:80], 'k', label='generate_test')\n",
    "plt.plot(df_index[TRAIN_END + 50: TRAIN_END + 80], (all_series.clone().numpy()* train_std + train_mean)[TRAIN_END + 50: TRAIN_END + 80], 'r', label='real_data')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
